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Record W2796433248 · doi:10.1002/cjce.23219

Characterization of the liquid fractions from textile sludge pyrolysis and their application as defoamers

2018· article· en· W2796433248 on OpenAlexvenueno aff
Ana Silvia Scheibe, Tarcísio Wolff Leal, H.L. Brandão, José Alexandre Borges Valle, Selene Maria de Arruda Guelli Ulson de Souza, Antônio Augusto Ulson de Souza

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
FundersBanco Nacional de Desenvolvimento Econômico e Social
KeywordsPyrolysisFourier transform infrared spectroscopyExtraction (chemistry)DefoamerGas chromatographyMass spectrometryChromatographyChemistryTextileMaterials scienceOrganic chemistryChemical engineering

Abstract

fetched live from OpenAlex

Abstract The aim of this study was to investigate the characteristics and the defoamer capacity of bio‐oils obtained from textile sludge pyrolysis. The pyrolysis was carried out at two temperatures: 310 and 500 °C, and the bio‐oils were analyzed after 7 days and after 2 months of storage under refrigeration. The structure of the bio‐oils was determined by Fourier transform infrared spectroscopy (FTIR) and the extraction of polar compounds by solid phase micro extraction (SPME) coupled with gas chromatography/mass spectrometry (GC/MS) analysis. According to the chromatography and FTIR results, aromatic hydrocarbons, amines, silicone, and organic sulphur compounds were present in the pyrolysis oils. The Bikerman test showed that 1 mL of bio‐oil obtained from pyrolysis at 500 °C can break down a column of foam in less than 1 min, which is comparable to the results of commercial antifoams. According to these results, these oils can be used as defoamers, even at the textile plant itself.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.004
GPT teacher head0.165
Teacher spread0.161 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2018
Admission routes1
Has abstractyes

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